Abstract
Path planning algorithms are of paramount importance in guidance and collision systems to provide trustworthiness and safety for operations of autonomous unmanned aerial vehicles (UAV). Previous works showed different approaches mostly focusing on shortest path discovery without a sufficient consideration on local planning and collision avoidance. In this paper, we propose a hybrid path planning algorithm that uses an anytime graph-based path planning algorithm for global planning and deep reinforcement learning for local planning which applied for a real-time mission planning system of an autonomous UAV. In particular, we aim to achieve a highly autonomous UAV mission planning system that is adaptive to real-world environments consisting of both static and moving obstacles for collision avoidance capabilities. To achieve adaptive behavior for real-world problems, a simulator is required that can imitate real environments for learning. For this reason, the simulator must be sufficiently flexible to allow the UAV to learn about the environment and to adapt to real-world conditions. In our scheme, the UAV first learns about the environment via a simulator, and only then is it applied to the real-world. The proposed system is divided into two main parts: optimal flight path generation and collision avoidance. A hybrid path planning approach is developed by combining a graph-based path planning algorithm with a learning-based algorithm for local planning to allow the UAV to avoid a collision in real time. The global path planning problem is solved in the first stage using a novel anytime incremental search algorithm called improved Anytime Dynamic A* (iADA*). A reinforcement learning method is used to carry out local planning between waypoints, to avoid any obstacles within the environment. The developed hybrid path planning system was investigated and validated in an AirSim environment. A number of different simulations and experiments were performed using AirSim platform in order to demonstrate the effectiveness of the proposed system for an autonomous UAV. This study helps expand the existing research area in designing efficient and safe path planning algorithms for UAVs.
Highlights
Real-time mission planning systems for autonomous vehicles such as unmanned aerial vehicles (UAV) have attracted widespread research interest over recent years
The global path planning algorithm must re-plan the path from the location provided by the local planning module
Randomized mission was assigned to the UAV, and as it flew along the planned path generated by the iADA* path planner, the Deep Q-Network (DQN) agent continually checked for dynamic obstacles
Summary
Real-time mission planning systems for autonomous vehicles such as UAVs have attracted widespread research interest over recent years. In these mission planning systems, path planning forms the main task, and dynamic path planning has been identified as one of the most important tasks for autonomous systems. Path planning, and machine learning algorithms have been proposed over the years to solve these problems for autonomous UAV systems [1,2,3]. The main challenges for real-time autonomous system are an efficient path planning and re-planning, ensure risk free for planning while avoiding collisions in the consideration of the UAV constraints such as velocity and/or maximum speed. The system should consider the safety in the environment which included static and dynamic moving obstacles
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